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1. Data science for everyone
1.1 What is data science?
1.2 Why data science?
1.3 Tools and techniques
1.3.1 Computational tools
1.3.2 Statistical techniques
1.4 Plotting the classics
1.4.1 Literary characters
1.4.2 Another kind of character
2. Programming
2.1 A sampling problem
2.2 A simpler problem
2.3 Expressions
2.4 Variables
2.5 Names
2.6 Call expressions
3. Data types
3.1 Numbers
3.2 Strings
3.3 Comparison
3.4 Arrays
3.5 Ranges
3.6 More on arrays
3.7 Arrays and axes
3.8 Reply to the Supreme Court
3.9 Revision - three girls
3.10 Selecting with arrays
4. Data frames
4.1 Introduction to data frames
5. Permutations
5.1 Population and permutation
5.2 lists
5.3 Iteration with For loops
5.4 Indentation, indentation
5.5 Ones and zeros
5.6 A permutation test
6. More on simulation
6.1 Sorting arrays
6.2 Monty hall problem
7. More building blocks
7.2 None
7.1 Functions
7.1 Functions as values
7.2 Conditional statements
8. The mean and straight line relationships
8.1 The mean as a predictor
8.2 Where and argmin
8.3 Mean and slopes
8.4 Optimization
8.5 Finding lines
8.6 Believable slopes
9. Classification
9.1 Standard scores
9.2 Nearest neighbors
9.3 Training and testing
9.4 Rows of tables
9.5 Implementing the classifier
9.6 Accuracy of the classifier
10. The end of the beginning
Exercises
Thinking about names
Three girl simulations
Array indexing
More simulations
Data frames
Brexit analysis
For loops
Money and death
Function exercises
Function as values exercises
Conditional statement exercises
Extra pages
More on lists
Monty Hall with lists
Berkeley introduction to functions
Deviations around the mean
Exercises
Some exercises for various points in the course.
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